---
title: "p8105_hw6_jh3909"
author: "Jingxuan He(UNI: jh3909),
         Yue Pan (UNI: )" 
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(janitor)
library(stringr)
library(forcats)
library(viridis)
library(plotly)
library(tidytext)
theme_set(theme_bw())

# Make some noisily increasing data
set.seed(955)
dat <- data.frame(cond = rep(c("A", "B"), each=10),
                  xvar = 1:20 + rnorm(20,sd=3),
                  yvar = 1:20 + rnorm(20,sd=3))
```


```{r}
set.seed(1)

NYC_restaurant_data = read_csv("DOHMH_New_York_City_Restaurant_Inspection_Results.csv.gz", col_types = cols(building = col_character()),
                           na = c("NA", "N/A")) %>%
  filter(grade %in% c("A", "B", "C")) %>% 
  mutate(inspection_num = row_number(),
         boro = str_to_title(boro)) %>% 
  filter(boro !="Missing") %>% 
  select(inspection_num, boro, grade, score, critical_flag, dba, cuisine_description,   zipcode, violation_description, inspection_date)

```


Row
-----------------------------------------------------------------------

### Chart A

```{r}
## histrogram of the number of restaurants in each boro
NYC_restaurant_data %>% 
  count(boro) %>% 
  mutate(boro = fct_reorder(boro, n)) %>% 
  plot_ly(y = ~n, color = ~boro, type = "bar") %>%
  layout(xaxis = list(title = "Boro"), yaxis = list(title = "Count"))
```

### Chart B

```{r}
# make a box plot; x = cuisine_descirption; y= score
```

Row
-----------------------------------------------------------------------

### Chart C

```{r}
# density plot that shows the number of rows for each inspection date
# NYC_restaurant_data %>% 
  # plot_ly(x = ~inspection, y = ~n, color = ~neighbourhood, type = "bar")


```

### Chart D

```{r}
# Sentiment Plot
```